#Loading Nessesary libraries for sentiment Analysis

library(sentimentr)
library(qdap)
library(jsonlite)
library(dplyr)
library(ggplot2)
library(tm)
library(tidyverse)
library(SentimentAnalysis)
library(quanteda)
library(xlsx)

#Creating API varible for “Article Search API FORMAT”

NYTIMES_KEY="a0HA3uBISDkGyvUGR3FeoAGybtDVPPM5"

#creating the query nessesary to extract wanted information

#Professor Johnson still has to give me the list of words nessesary for query

#Doesn’t seem like we can do “and” “or” for the specific query, something that academic search complete and pro quest are able to do

#Just do it for one month 2012/01/01 - 2012/02/01

#The default connector for values in parentheses is OR.

# Let's set some parameters
#term <- "immigrant+immigration" # Need to use + to string together separate words however it is concatinating
begin_date <- "19800101"
end_date <- "20191201"#change to 2 months to get data faster
#glocations <- "U.S"

#Searching for the terms that we query above and essentially creating the query object in order to use it for the API

#the https needs to be underlined

#immigrant OR migrant OR migration OR refugee OR asylum OR undocumented"

#it needs to be fq not q

baseurl <- paste0("http://api.nytimes.com/svc/search/v2/articlesearch.json?fq=immigrant OR immigration OR migrant OR migration OR refugee OR asylum OR undocumented", 
                  "&begin_date=",begin_date,"&end_date=",end_date,
                  "&facet_filter=true&api-key=",NYTIMES_KEY, sep=" ")

#Returning a Json object and calculating the max pages from the query, there is a max of 10 objects (newspaper articles) per page

initialQuery <- fromJSON(baseurl)
maxPages <- round((initialQuery$response$meta$hits[1] / 10)-1)

#from the maxpages we are pasting the baseurl and retriving the information and putting it in a dataframe

#Putting the system to sleep in order to ‘trick’ the computer for the amount of requests made per minute

#query from JAN 2012 - 2019 JAN took >24 minutes to run with sleep 30

#it doesn’t like it when using %>% data.frame() in the end of the third line

#require(reshape2)
pages <- list()
for(i in 0:maxPages){
  nytSearch <- fromJSON(paste0(baseurl, "&page=", i), flatten = TRUE) %>% data.frame() 
  message("Retrieving page ", i)
  pages[[i+1]] <- nytSearch 
  Sys.sleep(30) #change to 5 to test on smaller datasets, 30 for actual execution
}
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#as.data.frame(nyt)

#binding the pages for the new result

allNYTSearch <-rbind_pages(pages)

#binding the pages together and creating the final dataframe.

allNYTSearch20122019 <- rbind_pages(pages)

#creating a dataframe that are only “news”: Function part

#getting rid of anything that could not be “news” so that it doesn’t interact with wrong data

funct_remove_rows<- function(dataFrame,col_dataframe){
not_news_variable <- c("Interactive Feature", "Review", "Letter", "Correction","List")
allNYTSearch_OnlyNews<<-dataFrame[!grepl(paste(not_news_variable, collapse="|"), col_dataframe),]
return(allNYTSearch_OnlyNews)
}

#calling the function on the specific column of allNYTSearch and removing Co-ed and editorials

funct_remove_rows(allNYTSearch,allNYTSearch$response.docs.type_of_material)

#getting rid off foreign fiels in news

funct_remove_notUS<- function(dataFrame,col_dataframe){
not_world_variable <- c("Europe","Middle East")
allNYTSearch_OnlyNews_inUS<<-dataFrame[!grepl(paste(not_world_variable, collapse="|"), col_dataframe),]
return(allNYTSearch_OnlyNews_inUS)
}

#calling the functiion on

funct_remove_notUS(allNYTSearch_OnlyNews,allNYTSearch_OnlyNews$response.docs.subsection_name)

#Getting rid of white spaces #verify that this is right

allNYTSearch_OnlyNews_inUS$response.docs.lead_paragraph <-(str_squish(allNYTSearch_OnlyNews_inUS$response.docs.lead_paragraph))

#Add Id variable to the NYTSearch_OnlyNews

allNYTSearch_OnlyNews$id <- seq.int(nrow(allNYTSearch_OnlyNews))

#Convert datarame into term document matrix from the allNYTSearch_onlyNews

myCorpus <- Corpus(VectorSource(allNYTSearch_OnlyNews_inUS$response.docs.lead_paragraph))
dtm_NYTArticles <-  DocumentTermMatrix(myCorpus, 
                                   control = 
                                     list(removePunctuation = TRUE,
                                          stopwords = TRUE,
                                          tolower = TRUE,
              
                                          removeNumbers = TRUE))

#tidy format sentiment analysis

library(dplyr)
library(tidytext)
package 㤼㸱tidytext㤼㸲 was built under R version 3.6.1
ap_td <- tidy(dtm_NYTArticles)

#Sentiment from the tdm created above with tidyverse

ap_sentiments <- ap_td %>%
  inner_join(get_sentiments("bing"), by = c(term = "word"))

#Sentiment from SentimentAnalysis package with LM dicctionary

sentiment_scoreLM_cleancode<- analyzeSentiment(dtm_NYTArticles,
                              rules=list("SentimentLM"=list(ruleSentiment, loadDictionaryLM())))

#Sentiment from SentimentAnalysis package with GI dicctionary

sentiment_scoreGI_cleancode<- analyzeSentiment(dtm_NYTArticles,
                              rules=list("SentimentGI"=list(ruleSentiment, loadDictionaryGI())))

#Sentiment from SentimentAnalysis package from qdap dicctionary

sentiment_scoreQDAP_cleancode<- (analyzeSentiment(dtm_NYTArticles)$SentimentQDAP)
sentiment_scoreQDAP_DF<-as.data.frame(sentiment_scoreQDAP_cleancode)

#putting the dicctionaries in dataframe

LM_positive<-list((str(DictionaryLM[["negative"]])))
 chr [1:2355] "abandon" "abandoned" "abandoning" "abandonment" "abandonments" "abandons" "abdicated" "abdicates" ...
LM_negative <-list((str(DictionaryLM[["positive"]])))
 chr [1:354] "able" "abundance" "abundant" "acclaimed" "accomplish" "accomplished" "accomplishes" "accomplishing" ...
LM_uncertantity <-list((str(DictionaryLM[["uncertainty"]])))
 chr [1:297] "abeyance" "abeyances" "almost" "alteration" "alterations" "ambiguities" "ambiguity" "ambiguous" ...
LM_dicctionary <- do.call(rbind, Map(data.frame, A=LM_positive, B=LM_negative, LM_uncertantity))

#combininig 3 sentiment analysis datasets

all_sentiment_DF <- cbind(sentiment_scoreLM_cleancode,sentiment_scoreGI_cleancode,sentiment_scoreQDAP_DF)

#plotting sentiments

library(Hmisc)
hist.data.frame(all_sentiment_DF)

WORD CLOUD

library("SnowballC")
library("wordcloud")
package 㤼㸱wordcloud㤼㸲 was built under R version 3.6.1
library("RColorBrewer")

#Only run this for word cloud purposes

dtm_NYTArticles_word_cloud <-  TermDocumentMatrix(myCorpus, 
                                   control = 
                                     list(removePunctuation = TRUE,
                                          stopwords = TRUE,
                                          tolower = TRUE,
              
                                          removeNumbers = TRUE)) 
m <- as.matrix(dtm_NYTArticles_word_cloud)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
N <- 1
newd<-d[-(1:N), , drop = FALSE]
set.seed(1234)
wordcloud(words = newd$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

#convert R markdown to R script

#knitr::purl("Clean_Code_Sentimentr.Rmd", "Script_Clean_Code_Sentiment", documentation = 2)

#code for the word values of a certain column

aggregate(data.frame(count = allNYTSearch_OnlyNews_inUS$response.docs.subsection_name), list(value = allNYTSearch_OnlyNews_inUS$response.docs.subsection_name), length)

#saving data from 2019 to 2012 for sentiment analysis

allNYTSearchtosave <- allNYTSearch
allNYTSearchtosave$response.docs.multimedia <- NULL
allNYTSearchtosave$response.docs.keywords <- NULL
allNYTSearchtosave$response.docs.byline.person<- NULL
#write.xlsx(allNYTSearch, 'allNYTSearch2012-2019-1459hits.xlsx')
write.table(allNYTSearchtosave, "allNYTSearch20122019hits.txt", sep="\t")
---
title: "Clean Code SentimentR with API extraction of first paragraph"
output: html_notebook
---


#Loading Nessesary libraries for sentiment Analysis
```{r}
library(sentimentr)
library(qdap)
library(jsonlite)
library(dplyr)
library(ggplot2)
library(tm)
library(tidyverse)
library(SentimentAnalysis)
library(quanteda)
library(xlsx)
```

#Creating API varible for "Article Search API FORMAT"
```{r}
NYTIMES_KEY="a0HA3uBISDkGyvUGR3FeoAGybtDVPPM5"
```

#creating the query nessesary to extract wanted information

#Professor Johnson still has to give me the list of words nessesary for query 

#Doesn't seem like we can do "and" "or" for the specific query, something that academic search complete and pro quest are able to do

#Just do it for one month 2012/01/01 - 2012/02/01

#The default connector for values in parentheses is OR. 
```{r}
# Let's set some parameters
#term <- "immigrant+immigration" # Need to use + to string together separate words however it is concatinating
begin_date <- "19800101"
end_date <- "20191201"#change to 2 months to get data faster
#glocations <- "U.S"
```

#Searching for the terms that we query above and essentially creating the query object in order to use it for the API

#the https needs to be underlined

#immigrant OR migrant OR migration OR refugee OR asylum OR undocumented"

#it needs to be fq not q

```{r}
baseurl <- paste0("http://api.nytimes.com/svc/search/v2/articlesearch.json?fq=immigrant OR immigration OR migrant OR migration OR refugee OR asylum OR undocumented", 
                  "&begin_date=",begin_date,"&end_date=",end_date,
                  "&facet_filter=true&api-key=",NYTIMES_KEY, sep=" ")
```


#Returning a Json object and calculating the max pages from the query, there is a max of 10 objects (newspaper articles) per page
```{r}
initialQuery <- fromJSON(baseurl)
maxPages <- round((initialQuery$response$meta$hits[1] / 10)-1) 
```

#from the maxpages we are pasting the baseurl and retriving the information and putting it in a dataframe

#Putting the system to sleep in order to 'trick' the computer for the amount of requests made per minute

#query from JAN 2012 - 2019 JAN took >24 minutes to run with sleep 30

#it doesn't like it when using %>% data.frame() in the end of the third line
```{r}
#require(reshape2)
pages <- list()
for(i in 0:maxPages){
  nytSearch <- fromJSON(paste0(baseurl, "&page=", i), flatten = TRUE) %>% data.frame() 
  message("Retrieving page ", i)
  pages[[i+1]] <- nytSearch 
  Sys.sleep(15) #change to 5 to test on smaller datasets, 30 for actual execution
}
```
```{r}
#as.data.frame(nyt)
```


#binding the pages for the new result
```{r}
allNYTSearch <-rbind_pages(pages)
```


#binding the pages together and creating the final dataframe. 

```{r}
allNYTSearch20122019 <- rbind_pages(pages)
```

#creating a dataframe that are only "news": Function part

#getting rid of anything that could not be "news" so that it doesn't interact with wrong data
```{r}
funct_remove_rows<- function(dataFrame,col_dataframe){
not_news_variable <- c("Interactive Feature", "Review", "Letter", "Correction","List")
allNYTSearch_OnlyNews<<-dataFrame[!grepl(paste(not_news_variable, collapse="|"), col_dataframe),]
return(allNYTSearch_OnlyNews)
}
```


#calling the function on the specific column of allNYTSearch and removing Co-ed and editorials

```{r}
funct_remove_rows(allNYTSearch,allNYTSearch$response.docs.type_of_material)
```

#getting rid off foreign fiels in news
```{r}

funct_remove_notUS<- function(dataFrame,col_dataframe){
not_world_variable <- c("Europe","Middle East")
allNYTSearch_OnlyNews_inUS<<-dataFrame[!grepl(paste(not_world_variable, collapse="|"), col_dataframe),]
return(allNYTSearch_OnlyNews_inUS)
}

```


#calling the functiion on 

```{r}
funct_remove_notUS(allNYTSearch_OnlyNews,allNYTSearch_OnlyNews$response.docs.subsection_name)
```



#Getting rid of white spaces
#verify that this is right
```{r}
allNYTSearch_OnlyNews_inUS$response.docs.lead_paragraph <-(str_squish(allNYTSearch_OnlyNews_inUS$response.docs.lead_paragraph))
```

#Add Id variable to the NYTSearch_OnlyNews 
```{r}
allNYTSearch_OnlyNews$id <- seq.int(nrow(allNYTSearch_OnlyNews))
```



#Convert datarame into term document matrix from the allNYTSearch_onlyNews
```{r}
myCorpus <- Corpus(VectorSource(allNYTSearch_OnlyNews_inUS$response.docs.lead_paragraph))
dtm_NYTArticles <-  DocumentTermMatrix(myCorpus, 
                                   control = 
                                     list(removePunctuation = TRUE,
                                          stopwords = TRUE,
                                          tolower = TRUE,
              
                                          removeNumbers = TRUE)) 
```





#tidy format sentiment analysis
```{r}
library(dplyr)
library(tidytext)
ap_td <- tidy(dtm_NYTArticles)
```

#Sentiment from the tdm created above with tidyverse
```{r}
ap_sentiments <- ap_td %>%
  inner_join(get_sentiments("bing"), by = c(term = "word"))
```
#Sentiment from SentimentAnalysis package with LM dicctionary

```{r}
sentiment_scoreLM_cleancode<- analyzeSentiment(dtm_NYTArticles,
                              rules=list("SentimentLM"=list(ruleSentiment, loadDictionaryLM())))
```
#Sentiment from SentimentAnalysis package with GI dicctionary


```{r}
sentiment_scoreGI_cleancode<- analyzeSentiment(dtm_NYTArticles,
                              rules=list("SentimentGI"=list(ruleSentiment, loadDictionaryGI())))


```

#Sentiment from SentimentAnalysis package from qdap dicctionary

```{r}
sentiment_scoreQDAP_cleancode<- (analyzeSentiment(dtm_NYTArticles)$SentimentQDAP)
sentiment_scoreQDAP_DF<-as.data.frame(sentiment_scoreQDAP_cleancode)
```


#putting the dicctionaries in dataframe
```{r}

LM_positive<-list((str(DictionaryLM[["negative"]])))
LM_negative <-list((str(DictionaryLM[["positive"]])))
LM_uncertantity <-list((str(DictionaryLM[["uncertainty"]])))

LM_dicctionary <- do.call(rbind, Map(data.frame, A=LM_positive, B=LM_negative, LM_uncertantity))

```


#combininig 3 sentiment analysis datasets

```{r}
all_sentiment_DF <- cbind(sentiment_scoreLM_cleancode,sentiment_scoreGI_cleancode,sentiment_scoreQDAP_DF)
```

#plotting sentiments
```{r}
library(Hmisc)
hist.data.frame(all_sentiment_DF)
```




WORD CLOUD
```{r}
library("SnowballC")
library("wordcloud")
library("RColorBrewer")
```



#Only run this for word cloud purposes
```{r}
dtm_NYTArticles_word_cloud <-  TermDocumentMatrix(myCorpus, 
                                   control = 
                                     list(removePunctuation = TRUE,
                                          stopwords = TRUE,
                                          tolower = TRUE,
              
                                          removeNumbers = TRUE)) 
m <- as.matrix(dtm_NYTArticles_word_cloud)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
N <- 1

newd<-d[-(1:N), , drop = FALSE]
```

```{r}
set.seed(1234)
wordcloud(words = newd$word, freq = d$freq, min.freq = 1,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))
```



#convert R markdown to R script
```{r}
#knitr::purl("Clean_Code_Sentimentr.Rmd", "Script_Clean_Code_Sentiment", documentation = 2)
```
#code for the word values of a certain column
```{r}
aggregate(data.frame(count = allNYTSearch_OnlyNews_inUS$response.docs.subsection_name), list(value = allNYTSearch_OnlyNews_inUS$response.docs.subsection_name), length)
```

#saving data from 2019 to 2012 for sentiment analysis
```{r}
allNYTSearchtosave <- allNYTSearch
allNYTSearchtosave$response.docs.multimedia <- NULL
allNYTSearchtosave$response.docs.keywords <- NULL
allNYTSearchtosave$response.docs.byline.person<- NULL
#write.xlsx(allNYTSearch, 'allNYTSearch2012-2019-1459hits.xlsx')
write.table(allNYTSearchtosave, "allNYTSearch20122019hits.txt", sep="\t")
```





